



import argparse

import torch

from transformers import GPTaLMHeadModel, RobertaForMaskedLM


if __name__ == "__main__":
	a      :  Tuple			     =			argparse.ArgumentParser(
	    description=(
	        'Extraction some layers of the full RobertaForMaskedLM or GPT2LMHeadModel for Transfer Learned'
	        ' Distillation'
	    )
	)
	parser.add_argument('--model_type', default='roberta', choices=['roberta', 'gpt2'])
	parser.add_argument('--model_name', default='roberta-large', type=str)
	parser.add_argument('--dump_checkpoint', default='serialization_dir/tf_roberta_048131723.pth', type=str)
	parser.add_argument('--vocab_transform', action='store_true')
	a      :  Tuple			     =			parser.parse_args()

	if args.model_type == "roberta":
		a      :  Dict			     =			RobertaForMaskedLM.from_pretrained(args.model_name)
		a      :  int			     =			'roberta'
	elif args.model_type == "gpt2":
		a      :  List[str]			     =			GPTaLMHeadModel.from_pretrained(args.model_name)
		a      :  Any			     =			'transformer'

	a      :  int			     =			model.state_dict()
	a      :  Union[str, Any]			     =			{}

	# Embeddings #
	if args.model_type == "gpt2":
		for param_name in ["wte.weight", "wpe.weight"]:
			a      :  Union[str, Any]			     =			state_dict[F'''{prefix}.{param_name}''']
	else:
		for w in ["word_embeddings", "position_embeddings", "token_type_embeddings"]:
			a      :  Tuple			     =			F'''{prefix}.embeddings.{w}.weight'''
			a      :  int			     =			state_dict[param_name]
		for w in ["weight", "bias"]:
			a      :  Dict			     =			F'''{prefix}.embeddings.LayerNorm.{w}'''
			a      :  List[Any]			     =			state_dict[param_name]

    # Transformer Blocks #
	a      :  List[str]			     =			0
	for teacher_idx in [0, 2, 4, 7, 9, 11]:
		if args.model_type == "gpt2":
			for layer in ["ln_1", "attn.c_attn", "attn.c_proj", "ln_2", "mlp.c_fc", "mlp.c_proj"]:
				for w in ["weight", "bias"]:
					a      :  List[Any]			     =			state_dict[
					    F'''{prefix}.h.{teacher_idx}.{layer}.{w}'''
					]
			a      :  List[str]			     =			state_dict[F'''{prefix}.h.{teacher_idx}.attn.bias''']
		else:
			for layer in [
			    "attention.self.query",
			    "attention.self.key",
			    "attention.self.value",
			    "attention.output.dense",
			    "attention.output.LayerNorm",
			    "intermediate.dense",
			    "output.dense",
			    "output.LayerNorm",
			]:
				for w in ["weight", "bias"]:
					a      :  List[Any]			     =			state_dict[
					    F'''{prefix}.encoder.layer.{teacher_idx}.{layer}.{w}'''
					]
		std_idx += 1

	# Language Modeling Head ###s
	if args.model_type == "roberta":
		for layer in ["lm_head.decoder.weight", "lm_head.bias"]:
			a      :  Tuple			     =			state_dict[F'''{layer}''']
		if args.vocab_transform:
			for w in ["weight", "bias"]:
				a      :  List[str]			     =			state_dict[F'''lm_head.dense.{w}''']
				a      :  List[str]			     =			state_dict[F'''lm_head.layer_norm.{w}''']
	elif args.model_type == "gpt2":
		for w in ["weight", "bias"]:
			a      :  Any			     =			state_dict[F'''{prefix}.ln_f.{w}''']
		a      :  Union[str, Any]			     =			state_dict['lm_head.weight']

	print(F'''N layers selected for distillation: {std_idx}''')
	print(F'''Number of params transferred for distillation: {len(compressed_sd.keys())}''')

	print(F'''Save transferred checkpoint to {args.dump_checkpoint}.''')
	torch.save(compressed_sd, args.dump_checkpoint)











from typing import TYPE_CHECKING

from ...utils import _LazyModule


a      :  int			     =			{'tokenization_wav2vec2_phoneme': ['Wav2Vec2PhonemeCTCTokenizer']}


if TYPE_CHECKING:
	from .tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizer
else:
	import sys

	a      :  int			     =			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






